Experiments for the Poor Insurance Provision in Low-Income Communities Part II: Initial Lessons from Micro-Insurance Experiments for the Poor
Bibliographic record
Abstract
Project, contract number PCE-C-00-96-90004-00. Warren Brown has worked with the Research and Policy Unit at Calmeadow since May 1999. During his time at Calmeadow, Mr. Brown has been responsible for managing and conducting research for the Ford Foundation and USAID's MBP Research Facility on the current state of the practice in micro-insurance. Prior to coming to Calmeadow, he worked as a consultant for Monitor Company, an international strategy consultancy. While at Monitor, Mr. Brown worked with Canadian financial services clients in areas such as new market assessment, customer research, and product development. Craig F. Churchill is the Director of Calmeadow’s Research and Policy Unit. Based in Washington, D.C., he oversees Calmeadow’s various research initiatives as well as its renowned Resource Center. Prior to joining Calmeadow, Mr. Churchill was the Coordinator of the MicroFinance Network, a global association of leading microfinance practitioners. His microfinance experience also includes
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".